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A Unified, Probabilistic Framework for Structure- and Ligand-Based Virtual Screening
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DOI：10.1021/jm1013677.png)
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We present a probabilistic framework for interpreting structure-based virtual screening that returns a quantitative likelihood of observing bioactivity and can be quantitatively combined with ligand-based screening methods to yield a cumulative prediction that consistently outperforms any single screening metric. The approach has been developed and validated on more than 30 different protein targets. Transforming structure-based in silico screening results into robust probabilities of activity enables the general fusion of multiple structure-and ligand-based approaches and returns a quantitative expectation of success that can be used to prioritize (or deprioritize) further discovery activities. This unified probabilistic framework offers a paradigm shift in how docking and scoring results are interpreted, which can enhance early lead-finding efforts by maximizing the value of in silico computational tools.
Keyword:
MOLECULAR DOCKING
DATA FUSION
SCORING FUNCTIONS
POSE PREDICTION
SIMILARITY
SHAPE
INHIBITORS
POTENT
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6.8
论文数:
2.7W
被引数:
9.4W
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